Agriculture underpins rural livelihoods and economies across the Global South. However, crop yields achieved by smallholder farmers often fall far below agronomic potential. Limited or inefficient use of crop inputs – such as irrigation and fertilisers – is a key cause of crop yield gaps in smallholder farming systems. A lack of reliable information about expected yield and income benefits, along with farmers’ aversion to investments in inputs like fertiliser or fuel for irrigation, is one of the main underlying drivers of low or inefficient input use in smallholder farming systems.
Development of data-driven advisory products and services that effectively empower smallholder farmers to make more informed and precise input use decisions has the potential to be a game changer for rural development and climate change adaptation. However, while digitally enabled tools for precision agriculture have scaled rapidly over recent years in high-productivity agricultural systems in regions such as Europe and Australia, their success in smallholder farming systems has been much more limited, with most farmer-facing smart farming tools struggling to sustain outcomes at scale beyond the pilot phase of product development.
This PhD project will address these issues by developing new data-driven methods for precision input management in smallholder environments through integration of process-based crop modelling, advanced data analytics, and farm-level agronomic and socio-economic data stacks. The project will be focused on a case study of irrigation and fertiliser management under climate uncertainty in rice-wheat production systems in South Asia, where low levels of water and fertiliser use are the primary driver of yield gaps for millions of rural farmers. Key objectives of the PhD will be to:
- Critically evaluate the status, strengths and weaknesses of existing data driven DSS and advisory tools used to guide water and fertiliser input management in smallholder farming systems in South Asia.
- Develop a computationally efficient and reliable approach combining crop modelling (APSIM), machine learning and optimisation techniques for determining optimal allocation of limited water and fertiliser inputs in small-scale production systems in target regions.
- Evaluate yield, income and environmental outcomes of optimised water and fertiliser management strategies in comparison with existing farmer heuristics, and assess how these benefits are affected by differences in farm characteristics, production settings, model and input data uncertainty.
The student will be based in the Agriculture, Water and Climate group at The University of Manchester (www.ag-water.weebly.com). They will be supervised by Dr Tim Foster and Dr Ben Parkes, who together have extensive experience in crop simulation modelling and agricultural water management in South Asia and globally. Research will involve close collaboration and travel to engage with partner organisations in South Asia, including the International Maize and Wheat Improvement Centre (CIMMYT), who will provide access to critical field datasets and surveys to facilitate model testing, scenario design, and impact evaluation.
As part of the project, the student will spend a total of 12 months visiting the University of Melbourne. During the time in Melbourne, they will work with project partners at Melbourne specialising in fertiliser management (Dr Shu Kee Lam and Dr Alexis Pang) and agricultural data informatics (Prof Pablo Zaro-Tejada), in particular to support objectives 2 and 3 of the PhD. They will also have the opportunity to collaborate and engage with wider research networks at Melbourne, including a parallel dual-award PhD student based at the University of Melbourne whose research will focus on advancing crop nutrient monitoring and modelling for sustainable intensification of farming systems in Australia and globally.
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